1. Field of the Invention
The present invention relates to a system and method for detecting presence of a human in a vehicle, and a vehicle containing such a system.
2. Background Art
When a driver returns to his or her vehicle at night, particularly in a deserted location, the knowledge that no one is hiding inside the vehicle can provide peace of mind. In many cases, the ability to reliably detect the presence of a person inside a parked vehicle is desirable. Detecting the presence of a vehicle occupant is a process that has been used for some time at border crossings, or at the entrance to, or exit from, a secure site. In these cases, sensitive vibration sensors are often used to “listen” for the telltale vibrations of occupants hidden in a vehicle.
A method of computer processing the sensor outputs was developed at Oak Ridge National Laboratories, and was applied to screening vehicles entering and leaving nuclear sites and prison facilities. The systems developed by Oak Ridge National Laboratories used multiple geophones on the vehicle, and tested for 10-20 seconds, looking for the characteristic acoustic wave generated by a heartbeat. Such systems were designed to give no false negatives—i.e., reporting the vehicle unoccupied when someone is actually in it—at the expense of having some false positives—i.e., reporting the vehicle occupied when no one is actually in it. In particular, these systems can be sensitive to false positives in windy conditions.
In addition to the systems used at border crossings and other secure sites, human detection systems have also been used as part of various other vehicle systems, such as controlling an occupant restraint system. One such method and apparatus is described in U.S. Patent Application Publication No. 2004/0039509, applied for by Breed, and published on Feb. 26, 2004. The method and apparatus described in Breed senses the occupancy of a vehicle using various sensors. In order to differentiate between different occupant conditions—e.g., a rear-facing child seat and a forward-facing occupant—a neural network is trained under a variety of experimental conditions so that the system can differentiate between the different conditions when the system is operating. In fact, Breed notes that as many as 1,000,000 experiments may need to be run before the network is sufficiently trained.
One limitation of the method and apparatus described in Breed is that the neural network includes feedforward nodes that do not exhibit state. In contrast, the use of a neural network having at least some recurrent nodes may provide a number of advantages. For example, having recurrent nodes provides a means for directing output from a node back into itself. This can increase the accuracy of the output, and greatly speed the learning process of the network, thereby significantly reducing the number of experiments required before the network can be operated. In addition, having a neural network that utilizes recurrent nodes can provide a time delay between the output from one of the recurrent nodes and its input back into itself, or its input into another node. This allows the multiple inputs into a node to be combined prior to being processed by the node. This also can greatly increase the speed at which the network is trained and increase the accuracy of the output.